Amortized Object and Scene Perception for Long-term Robot Manipulation

Mobile robots, performing long-term manipulation activities in human environments, have to perceive a wide variety of objects possessing very different visual characteristics and need to reliably keep track of these throughout the execution of a task. In order to be efficient, robot perception capabilities need to go beyond what is currently perceivable and should be able to answer queries about both current and past scenes. In this paper we investigate a perception system for long-term robot manipulation that keeps track of the changing environment and builds a representation of the perceived world. Specifically we introduce an amortized component that spreads perception tasks throughout the execution cycle. The resulting query driven perception system asynchronously integrates results from logged images into a symbolic and numeric (what we call sub-symbolic) representation that forms the perceptual belief state of the robot

RoboSherlock: Cognition-enabled Robot Perception for Everyday Manipulation Tasks

A pressing question when designing intelligent autonomous systems is how to integrate the various subsystems concerned with complementary tasks. More specifically, robotic vision must provide task-relevant information about the environment and the objects in it to various planning related modules. In most implementations of the traditional Perception-Cognition-Action paradigm these tasks are treated as quasi-independent modules that function as black boxes for each other. It is our view that perception can benefit tremendously from a tight collaboration with cognition. We present RoboSherlock, a knowledge-enabled cognitive perception systems for mobile robots performing human-scale everyday manipulation tasks. In RoboSherlock, perception and interpretation of realistic scenes is formulated as an unstructured information management(UIM) problem. The application of the UIM principle supports the implementation of perception systems that can answer task-relevant queries about objects in a scene, boost object recognition performance by combining the strengths of multiple perception algorithms, support knowledge-enabled reasoning about objects and enable automatic and knowledge-driven generation of processing pipelines. We demonstrate the potential of the proposed framework through feasibility studies of systems for real-world scene perception that have been built on top of the framework